VAD depends on discriminative representations that clearly represent the events in a scene, while action recognition datasets pretrained feature encoders are not perfect for surveillance videos because of the existence of a domain gap [1]}, [2]}, [3]}.
To address this problem, we introduce a self-training strategy to refine the [rgb]0.,0,0proposed improved feature encoder \(E_{SGA}\) . An illustration of our method shown in Figure REF  is detailed in the following.
[t]
[rgb]0,0.,0Multiple instance self-training framework

[1]
Clip-level labeled normal videos \(V^n = \lbrace v^n_i\rbrace ^N_{i=1}\)  and corresponding clip-level labels \(Y^n\) , video-level labeled abnormal videos \(V^a = \lbrace v^a_i\rbrace ^N_{i=1}\) , pretrained vanilla feature encoder \(E\) .
Self-guided attention boosted feature encoder \(E_{SGA}\) , multiple instance pseudo label generator \(G\) , clip-level pseudo labels \(\hat{Y}^a\)  for \(V^a\)
